2022
DOI: 10.3390/life12060776
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Exploring and Identifying Prognostic Phenotypes of Patients with Heart Failure Guided by Explainable Machine Learning

Abstract: Identifying patient prognostic phenotypes facilitates precision medicine. This study aimed to explore phenotypes of patients with heart failure (HF) corresponding to prognostic condition (risk of mortality) and identify the phenotype of new patients by machine learning (ML). A unsupervised ML was applied to explore phenotypes of patients in a derivation dataset (n = 562) based on their medical records. Thereafter, supervised ML models were trained on the derivation dataset to classify these identified phenotyp… Show more

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Cited by 5 publications
(7 citation statements)
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“…We found 34 eligible clustering studies that were performed between 2012 and 2022, and used varying datatypes, clustering methods, and sample sizes (Table 1 ) [ 10 , 17 33 , 34 •, 35 38 , 39 •, 40 42 , 43 •, 44 49 ]. Clustering techniques that were used included hierarchical clustering ( n = 14) [ 10 , 22 , 24 , 25 , 27 , 28 , 31 , 34 •, 36 , 39 •, 40 , 45 , 47 , 49 ], LCA ( n = 10) [ 17 , 21 , 26 , 32 , 33 , 35 , 37 , 43 •, 46 , 48 ], PAM ( n = 5) [ 19 , 29 , 30 , 34 •, 38 ], k-means clustering ( n = 5) [ 23 , 34 •, 41 , 42 , 44 ], and model-based clustering ( n = 3) [ 18 , 20 , 34 •]. Dataset sizes ranged from 103 patients to 318,384 patients.…”
Section: Resultsmentioning
confidence: 99%
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“…We found 34 eligible clustering studies that were performed between 2012 and 2022, and used varying datatypes, clustering methods, and sample sizes (Table 1 ) [ 10 , 17 33 , 34 •, 35 38 , 39 •, 40 42 , 43 •, 44 49 ]. Clustering techniques that were used included hierarchical clustering ( n = 14) [ 10 , 22 , 24 , 25 , 27 , 28 , 31 , 34 •, 36 , 39 •, 40 , 45 , 47 , 49 ], LCA ( n = 10) [ 17 , 21 , 26 , 32 , 33 , 35 , 37 , 43 •, 46 , 48 ], PAM ( n = 5) [ 19 , 29 , 30 , 34 •, 38 ], k-means clustering ( n = 5) [ 23 , 34 •, 41 , 42 , 44 ], and model-based clustering ( n = 3) [ 18 , 20 , 34 •]. Dataset sizes ranged from 103 patients to 318,384 patients.…”
Section: Resultsmentioning
confidence: 99%
“…Dataset sizes ranged from 103 patients to 318,384 patients. Datatypes varied between registry-based data ( n = 6) [ 19 , 26 , 27 , 42 , 43 •, 46 ], cohort data ( n = 7) [ 20 , 22 , 24 , 28 , 30 , 38 , 41 ], EHR data ( n = 9) [ 10 , 23 , 29 , 31 , 34 •, 44 , 47 49 ], and trial data ( n = 12) [ 17 , 18 , 21 , 25 , 32 , 33 , 35 37 , 39 •, 40 , 45 ], using varying variable types for the clustering such as clinical variables ( n = 31), echocardiographic variables ( n = 7) [ 10 , 18 , 20 , 22 , 23 , 40 , 49 ], biomarkers ( n = 4) [ 24 , 28 , 38 , 41 ], hemodynamic parameters ( n = 1) [ 23 ], and demographic variables ( n = 1) [ 27 ]. The number of variables used for analysis also varied between 8 and 415, and the number of clusters discovered ranged between 2 to 15.…”
Section: Resultsmentioning
confidence: 99%
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“…The data collection was the same as that explained in a previous study [ 14 ]. Medical records of patients with HF hospitalized at Toho University Ohashi Medical Center between April 2016 and March 2019 were reviewed by cardiovascular specialists, constituting a development dataset.…”
Section: Materials and Methodsmentioning
confidence: 99%